One of the common and promising deep learning approaches used for medical
image segmentation is transformers, as they can capture long-range dependencies
among the pixels by utilizing self-attention. Despite being successful in
medical image segmentation, transformers face limitations in capturing local
contexts of pixels in multimodal dimensions. We propose a Medical Image
Segmentation Transformer (MIST) incorporating a novel Convolutional Attention
Mixing (CAM) decoder to address this issue. MIST has two parts: a pre-trained
multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded
feature representation is passed through the CAM decoder for segmenting the
images. In the CAM decoder, an attention-mixer combining multi-head
self-attention, spatial attention, and squeeze and excitation attention modules
is introduced to capture long-range dependencies in all spatial dimensions.
Moreover, to enhance spatial information gain, deep and shallow convolutions
are used for feature extraction and receptive field expansion, respectively.
The integration of low-level and high-level features from different network
stages is enabled by skip connections, allowing MIST to suppress unnecessary
information. The experiments show that our MIST transformer with CAM decoder
outperforms the state-of-the-art models specifically designed for medical image
segmentation on the ACDC and Synapse datasets. Our results also demonstrate
that adding the CAM decoder with a hierarchical transformer improves
segmentation performance significantly. Our model with data and code is
publicly available on GitHub.

提出了一种 Medical Image Segmentation Transformer (MIST) 用于医学图像分割，其中包括一个新型的 Convolutional Attention Mixing (CAM) decoder，通过 skip connections 抑制不必要的信息，实验结果在 ACDC 和 Synapse 数据集上显示，我们的 MIST transformer with CAM decoder 优于专门设计用于医学图像分割的最先进模型。

MIST：具有卷积注意力混合（CAM）解码器的医学图像分割变压器

MIST: Medical Image Segmentation Transformer with Convolutional  Attention Mixing (CAM) Decoder

The standard non-invasive imaging technique used to assess the severity and
extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography
Angiography (CCTA). However, manual grading of each patient's CCTA according to
the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and
operator-dependent, especially in borderline cases. This work proposes a fully
automated, and visually explainable, deep learning pipeline to be used as a
decision support system for the CAD screening procedure. The pipeline performs
two classification tasks: firstly, identifying patients who require further
clinical investigations and secondly, classifying patients into subgroups based
on the degree of stenosis, according to commonly used CAD-RADS thresholds. The
pipeline pre-processes multiplanar projections of the coronary arteries,
extracted from the original CCTAs, and classifies them using a fine-tuned
Multi-Axis Vision Transformer architecture. With the aim of emulating the
current clinical practice, the model is trained to assign a per-patient score
by stacking the bi-dimensional longitudinal cross-sections of the three main
coronary arteries along channel dimension. Furthermore, it generates visually
interpretable maps to assess the reliability of the predictions. When run on a
database of 1873 three-channel images of 253 patients collected at the Monzino
Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for
the two classification tasks, respectively. According to our knowledge, this is
the first model trained to assign CAD-RADS scores learning solely from patient
scores and not requiring finer imaging annotation steps that are not part of
the clinical routine.

本研究提出了一种快速、准确的冠状动脉疾病筛查和评估方法，基于深度学习和多轴视觉变压器架构，自动对计算机断层扫描成像进行分类和评分，并不需要人工干预。